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Record W2007763351 · doi:10.1083/jcb.200804098

The nuclear export factor Xpo1p targets Mad1p to kinetochores in yeast

2009· article· en· W2007763351 on OpenAlexaff
Robert J. Scott, Lucas V. Cairo, David W. Van de Vosse, Richard W. Wozniak

Bibliographic record

VenueThe Journal of Cell Biology · 2009
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicrotubule and mitosis dynamics
Canadian institutionsUniversity of Alberta
FundersHoward Hughes Medical Institute
KeywordsBiologyKinetochoreYeastNuclear export signalCell biologySaccharomyces cerevisiaeBudding yeastGeneticsNuclear proteinCell nucleusComputational biologyTranscription factorNucleusGeneChromosome

Abstract

fetched live from OpenAlex

Nuclear pore complexes (NPCs) mediate all nucleocytoplasmic traffic and provide docking sites for the spindle assembly checkpoint (SAC) protein Mad1p. Upon SAC activation, Mad1p is recruited onto kinetochores and rapidly cycles between NPCs and kinetochores. We examined the mechanism of Mad1p movement onto kinetochores and show that it is controlled by two components of the nuclear transport machinery, the exportin Xpo1p and Ran-guanosine triphosphate (GTP). Mad1p contains a nuclear export signal (NES) that is recognized by Xpo1p. The NES, Xpo1p, and RanGTP are all required for Mad1p recruitment onto kinetochores in checkpoint-activated cells. Consistent with this function, Xpo1p also accumulates on kinetochores after SAC activation. We have also shown that Xpo1p and RanGTP are required for the dynamic cycling of Mad1p between NPCs and kinetochores in checkpoint-arrested cells. These results reveal an important function for Xpo1p in mediating intranuclear transport events and identify a signaling pathway between kinetochores and NPCs.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.006
GPT teacher head0.233
Teacher spread0.227 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations22
Published2009
Admission routes1
Has abstractyes

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